Auxiliary Optimization Method for Electric Power Material Detection Based on Knowledge Graph
The multi-source,heterogeneous nature of the accumulated data from power material inspection operations makes ef-ficient analysis and mining challenging.Traditional material inspection methods are cumbersome and time-consuming,leading to increased costs and workloads.This paper focuses on the lengthy and inefficient inspection process for secondary equipment in power materials.It proposes a knowledge extraction,transformation,and application scheme that integrates named entity recog-nition and knowledge graph technologies.It introduces an enhanced method using the pre-trained BERT model to extract textual information on defects in secondary equipment.Furthermore,it employs knowledge graph technology to represent and integrate heterogeneous power material knowledge across domains.Ultimately,an intelligent recommendation system for secondary e-quipment inspection is developed.Based on knowledge graph and association analysis reasoning,the system generates inspection plan recommendations,thereby streamlining and optimizing the inspection process for power secondary equipment under similar business requirements.
NETL
NSTL
万方数据
